tbuckley/cabot-search huggingface dataset:
Serum Cystatin C for Estimation of GFR
Authors: Michael G. Shlipak, Lesley A. Inker, Josef Coresh
Journal: JAMA (2022)

An 80-year-old man with type 2 diabetes and hypertension was referred to the nephrology clinic for persistent hyperkalemia. The patient was frail and sedentary, with a BMI of 37. His estimated glomerular filtration rate calculated using creatinine was 64 mL/min/1.73 m2. How would you interpret these results?
Development and Validation of a Deep Learning Model for Brain Tumor Diagnosis and Classification Using Magnetic Resonance Imaging
Authors: Peiyi Gao, Wei Shan, Yue Guo, Yinyan Wang, Rujing Sun, Jinxiu Cai, Hao Li, Wei Sheng Chan, Pan Liu, Lei Yi, Shaosen Zhang, Weihua Li, Tao Jiang, Kunlun He, Zhenzhou Wu
Journal: JAMA Network Open (2022)

<h3>Importance</h3> Deep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. <h3>Objective</h3> To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. <h3>Design, Setting, and Participants</h3> This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1- and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. <h3>Main Outcomes and Measures</h3> Changes in neuroradiologist clinical diagnostic accuracy in brain MRI scans with vs without the deep learning system were evaluated. <h3>Results</h3> A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers’ data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan Hospital test data set: accuracy, 73.3% [95% CI, 67.7%-77.7%] vs 60.9% [95% CI, 46.8%-75.1%]; sensitivity, 88.9% [95% CI, 85.3%-92.4%] vs 53.4% [95% CI, 41.8%–64.9%]; and specificity, 96.3% [95% CI, 94.2%-98.4%] vs 97.9%; [95% CI, 97.3%-98.5%]). With the assistance of the deep learning system, the mean accuracy of neuroradiologists among 1166 patients increased by 12.0 percentage points, from 63.5% (95% CI, 60.7%-66.2%) without assistance to 75.5% (95% CI, 73.0%-77.9%) with assistance. <h3>Conclusions and Relevance</h3> These findings suggest that deep learning system–based automated diagnosis may be associated with improved classification and diagnosis of intracranial tumors from MRI data among neuroradiologists.
JAMA Neurology
Journal: JAMA Neurology (2022)

The mission of JAMA Neurology is to publish and disseminate scientific information primarily important for physicians caring for people with neurologic disorders and for those interested in the structure and function of the normal and diseased human nervous system.The specifi c aims are to (1) publish timely original research, including clinical trials that will directly improve clinical neurologic care and that will inform efforts to improve neurological health and promote health care equity; (2) report translational research that is pertinent to the understanding of neurologic disease; (3) address topics of practice, ethics, education, and public health that are a key part of modern medicine; and (4) provide a forum for discussion and publication of important topics including bias, racism, and diversity.This information will be published only after extensive review by scientifi c peers and journal editors so that clarity, rigor, originality, and precision are ensured.
Levodopa Content of <i>Mucuna pruriens</i> Supplements in the NIH Dietary Supplement Label Database
Authors: Pieter A. Cohen, Bharathi Avula, Kumar Katragunta, Ikhlas Khan
Journal: JAMA Neurology (2022)

This case series assesses the levels of levodopa in Mucuna pruriens supplements available in the US that are included in the National Institutes of Health Dietary Supplement Label Database.
Do Not Panic About Prenatal Exposure to Antipsychotics Except for Aripiprazole?—Reply
Authors: Loreen Straub, Sonia Hernández-Díaz, Krista F. Huybrechts
Journal: JAMA Internal Medicine (2022)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Internal Medicine HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Internal Medicine journal
Global Prevalence and Incidence of Tinnitus
Authors: Carlotta M. Jarach, Alessandra Lugo, Marco Scala, Piet A. van den Brandt, Christopher R. Cederroth, Anna Odone, Werner Garavello, Winfried Schlee, Berthold Langguth, Silvano Gallus
Journal: JAMA Neurology (2022)

Importance To date, no systematic review has taken a meta-analytic approach to estimating the prevalence and incidence of tinnitus in the general population. Objective To provide frequency estimates of tinnitus worldwide. Data Sources An umbrella review followed by a traditional systematic review was performed by searching PubMed-MEDLINE and Embase from inception through November 19, 2021. Study Selection Research data from the general population were selected, and studies based on patients or on subgroups of the population with selected lifestyle habits were excluded. No restrictions were applied according to date, age, sex, and country. Data Extraction and Synthesis Relevant extracted information included type of study, time and location, end point, population characteristics, and tinnitus definition. The study followed the Meta-analysis of Observational Studies in Epidemiology ( MOOSE ) reporting guideline. Main Outcomes and Measures Pooled prevalence estimates of any tinnitus, severe tinnitus, chronic tinnitus, and diagnosed tinnitus as well as incidence of tinnitus were obtained using random-effects meta-analytic models; heterogeneity between studies was controlled using the χ 2 test, and inconsistency was measured using the I 2 statistic. Results Among 767 publications, 113 eligible articles published between 1972 and 2021 were identified, and prevalence estimates from 83 articles and incidence estimates from 12 articles were extracted. The pooled prevalence of any tinnitus among adults was 14.4% (95% CI, 12.6%-16.5%) and ranged from 4.1% (95% CI, 3.7%-4.4%) to 37.2% (95% CI, 34.6%-39.9%). Prevalence estimates did not significantly differ by sex (14.1% [95% CI, 11.6%-17.0%] among male individuals; 13.1% [95% CI, 10.5%-16.2%] among female individuals), but increased prevalence was associated with age (9.7% [95% CI, 7.4%-12.5%] among adults aged 18-44 years; 13.7% [95% CI, 11.0%-17.0%] among those aged 45-64 years; and 23.6% [95% CI, 19.4%-28.5%] among those aged ≥65 years; P &amp;amp;lt; .001 among age groups). The pooled prevalence of severe tinnitus was 2.3% (95% CI, 1.7%-3.1%), ranging from 0.5% (95% CI, 0.3%-0.7%) to 12.6% (95% CI, 11.1%-14.1%). The pooled prevalence of chronic tinnitus was 9.8% (95% CI, 4.7%-19.3%) and the pooled prevalence of diagnosed tinnitus was 3.4% (95% CI, 2.1%-5.5%). The pooled incidence rate of any tinnitus was 1164 per 100 000 person-years (95% CI, 479-2828 per 100 000 person-years). Conclusions and Relevance Despite the substantial heterogeneity among studies, this comprehensive systematic review on the prevalence and incidence of tinnitus suggests that tinnitus affects more than 740 million adults globally and is perceived as a major problem by more than 120 million people, mostly aged 65 years or older. Health policy makers should consider the global burden of tinnitus, and greater effort should be devoted to boost research on tinnitus.
Sex and Location Differences in Verification Status of Physician-Held Social Media Platform Accounts
Authors: Deborah Rupert, Kanan Shah, Brian Y. Chen, Avital Y. O’Glasser, Michael Schiml, Shikha Jain, Fumiko Chino
Journal: JAMA Network Open (2022)

This cross-sectional study investigated the association of user sex and location with verification of physician-held social media accounts.
Development and Validation of Machine Models Using Natural Language Processing to Classify Substances Involved in Overdose Deaths
Authors: David Goodman-Meza, Chelsea L. Shover, Jesus A. Medina, Amber B. Tang, Steven Shoptaw, Alex A. T. Bui
Journal: JAMA Network Open (2022)

<h3>Importance</h3> Overdose is one of the leading causes of death in the US; however, surveillance data lag considerably from medical examiner determination of the death to reporting in national surveillance reports. <h3>Objective</h3> To automate the classification of deaths related to substances in medical examiner data using natural language processing (NLP) and machine learning (ML). <h3>Design, Setting, and Participants</h3> Diagnostic study comparing different natural language processing and machine learning algorithms to identify substances related to overdose in 10 health jurisdictions in the US from January 1, 2020, to December 31, 2020. Unstructured text from 35 433 medical examiner and coroners' death records was examined. <h3>Exposures</h3> Text from each case was manually classified to a substance that was related to the death. Three feature representation methods were used and compared: text frequency–inverse document frequency (TF-IDF), global vectors for word representations (GloVe), and concept unique identifier (CUI) embeddings. Several ML algorithms were trained and best models were selected based on F-scores. The best models were tested on a hold-out test set and results were reported with 95% CIs. <h3>Main Outcomes and Measures</h3> Text data from death certificates were classified as any opioid, fentanyl, alcohol, cocaine, methamphetamine, heroin, prescription opioid, and an aggregate of other substances. Diagnostic metrics and 95% CIs were calculated for each combination of feature extraction method and machine learning classifier. <h3>Results</h3> Of 35 433 death records analyzed (decedent median age, 58 years [IQR, 41-72 years]; 24 449 [69%] were male), the most common substances related to deaths included any opioid (5739 [16%]), fentanyl (4758 [13%]), alcohol (2866 [8%]), cocaine (2247 [6%]), methamphetamine (1876 [5%]), heroin (1613 [5%]), prescription opioids (1197 [3%]), and any benzodiazepine (1076 [3%]). The CUI embeddings had similar or better diagnostic metrics compared with word embeddings and TF-IDF for all substances except alcohol. ML classifiers had perfect or near perfect performance in classifying deaths related to any opioids, heroin, fentanyl, prescription opioids, methamphetamine, cocaine, and alcohol. Classification of benzodiazepines was suboptimal using all 3 feature extraction methods. <h3>Conclusions and Relevance</h3> In this diagnostic study, NLP/ML algorithms demonstrated excellent diagnostic performance at classifying substances related to overdoses. These algorithms should be integrated into workflows to decrease the lag time in reporting overdose surveillance data.
Do Not Panic About Prenatal Exposure to Antipsychotics Except for Aripiprazole?
Authors: Huang-Lung Tsou, Tien-Wei Hsu, Chih-Sung Liang
Journal: JAMA Internal Medicine (2022)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Internal Medicine HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Internal Medicine journal
Time-Restricted Eating to Improve Health—A Promising Idea in Need of Stronger Clinical Trial Evidence
Authors: Shalender Bhasin
Journal: JAMA Internal Medicine (2022)

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Internal Medicine HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Internal Medicine journal
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Authors: Francesca Bovis, Marta Ponzano, Alessio Signori, Irene Schiavetti, Paolo Bruzzi, Maria Sormani, Sormani Ponzano, Sormani Bruzzi
Journal: JAMA Neurology (2022)

sults observed in adults cannot be directly applied to children, 2 down-weighting factors 6 were applied (50% and 25%) to limit its influence on the results of the analyses with little effect on conclusions.
cfDNA methylome profiling for detection and subtyping of small cell lung cancers
Authors: Francesca Chemi, Simon P. Pearce, Alexandra Clipson, Steven M. Hill, Alicia-Marie Conway, Sophie A. Richardson, Katarzyna Kamieniecka, Rebecca Caeser, Daniel J. White, Sumitra Mohan, Victoria Foy, Kathryn L. Simpson, Melanie Galvin, Kristopher K. Frese, Lynsey Priest, Jacklynn Egger, Alastair Kerr, Pierre P. Massion, John T. Poirier, Gerard Brady, Fiona Blackhall, Dominic G. Rothwell, Charles M. Rudin, Caroline Dive
Journal: Nature Cancer (2022)

Small cell lung cancer (SCLC) is characterized by morphologic, epigenetic and transcriptomic heterogeneity. Subtypes based upon predominant transcription factor expression have been defined that, in mouse models and cell lines, exhibit potential differential therapeutic vulnerabilities, with epigenetically distinct SCLC subtypes also described. The clinical relevance of these subtypes is unclear, due in part to challenges in obtaining tumor biopsies for reliable profiling. Here we describe a robust workflow for genome-wide DNA methylation profiling applied to both patient-derived models and to patients' circulating cell-free DNA (cfDNA). Tumor-specific methylation patterns were readily detected in cfDNA samples from patients with SCLC and were correlated with survival outcomes. cfDNA methylation also discriminated between the transcription factor SCLC subtypes, a precedent for a liquid biopsy cfDNA-methylation approach to molecularly subtype SCLC. Our data reveal the potential clinical utility of cfDNA methylation profiling as a universally applicable liquid biopsy approach for the sensitive detection, monitoring and molecular subtyping of patients with SCLC.
A lab-on-a-chip for the concurrent electrochemical detection of SARS-CoV-2 RNA and anti-SARS-CoV-2 antibodies in saliva and plasma
Authors: Devora Najjar, Joshua Rainbow, Sanjay Sharma Timilsina, Pawan Jolly, Helena de Puig, Mohamed Yafia, Nolan Durr, Hani Sallum, Galit Alter, Jonathan Z. Li, Xu G. Yu, David R. Walt, Joseph A. Paradiso, Pedro Estrela, James J. Collins, Donald E. Ingber
Journal: Nature Biomedical Engineering (2022)
Generation of T-cell-receptor-negative CD8αβ-positive CAR T cells from T-cell-derived induced pluripotent stem cells
Authors: Sjoukje J. C. van der Stegen, Pieter L. Lindenbergh, Roseanna M. Petrovic, Hongyao Xie, Mame P. Diop, Vera Alexeeva, Yuzhe Shi, Jorge Mansilla-Soto, Mohamad Hamieh, Justin Eyquem, Annalisa Cabriolu, Xiuyan Wang, Ramzey Abujarour, Tom Lee, Raedun Clarke, Bahram Valamehr, Maria Themeli, Isabelle Riviere, Michel Sadelain
Journal: Nature Biomedical Engineering (2022)
HIC2 controls developmental hemoglobin switching by repressing BCL11A transcription
Authors: Peng Huang, Scott A. Peslak, Ren Ren, Eugene Khandros, Kunhua Qin, Cheryl A. Keller, Belinda Giardine, Henry W. Bell, Xianjiang Lan, Malini Sharma, John R. Horton, Osheiza Abdulmalik, Stella T. Chou, Junwei Shi, Merlin Crossley, Ross C. Hardison, Xiaodong Cheng, Gerd A. Blobel
Journal: Nature Genetics (2022)
Pepinemab antibody blockade of SEMA4D in early Huntington’s disease: a randomized, placebo-controlled, phase 2 trial
Authors: Andrew Feigin, Elizabeth E. Evans, Terrence L. Fisher, John E. Leonard, Ernest S. Smith, Alisha Reader, Vikas Mishra, Richard Manber, Kimberly A. Walters, Lisa Kowarski, David Oakes, Eric Siemers, Karl D. Kieburtz, Maurice Zauderer, Elise Kayson, Jody Goldstein, Richard Barbano, Karen Marder, Praveen Dayalu, Herminia Diana Rosas, Sandra Kostyk, John Kamholz, Brad Racette, Jee Bang, Daniel Claassen, Katherine McDonell, Stewart Factor, Francis Walker, Clarisse Goas, Joanne Wojcieszek, Lynn A. Raymond, Jody Corey-Bloom, Victor Sung, Marissa Dean, Michael Geshwind, Alexandra Nelson, Samuel Frank, Kathrin LaFaver, Andrew Duker, Lawrence Elmer, Ali Samii, Yi-Han Lin, Sylvain Chouinard, Lauren Seeberger, Burton Scott, James Boyd, Nikolaus McFarland, Erin Furr Stimming, Oksana Suchowersky, Claudia Testa, Karen Anderson
Journal: Nature Medicine (2022)

Abstract SIGNAL is a multicenter, randomized, double-blind, placebo-controlled phase 2 study (no. NCT02481674) established to evaluate pepinemab, a semaphorin 4D (SEMA4D)-blocking antibody, for treatment of Huntington’s disease (HD). The trial enrolled a total of 265 HD gene expansion carriers with either early manifest (EM, n = 179) or late prodromal (LP, n = 86) HD, randomized (1:1) to receive 18 monthly infusions of pepinemab ( n = 91 EM, 41 LP) or placebo ( n = 88 EM, 45 LP). Pepinemab was generally well tolerated, with a relatively low frequency of serious treatment-emergent adverse events of 5% with pepinemab compared to 9% with placebo, including both EM and LP participants. Coprimary efficacy outcome measures consisted of assessments within the EM cohort of (1) a two-item HD cognitive assessment family comprising one-touch stockings of Cambridge (OTS) and paced tapping (PTAP) and (2) clinical global impression of change (CGIC). The differences between pepinemab and placebo in mean change (95% confidence interval) from baseline at month 17 for OTS were −1.98 (−4.00, 0.05) (one-sided P = 0.028), and for PTAP 1.43 (−0.37, 3.23) (one-sided P = 0.06). Similarly, because a significant treatment effect was not observed for CGIC, the coprimary endpoint, the study did not meet its prespecified primary outcomes. Nevertheless, a number of other positive outcomes and post hoc subgroup analyses—including additional cognitive measures and volumetric magnetic resonance imaging and fluorodeoxyglucose–positron-emission tomography imaging assessments—provide rationale and direction for the design of a phase 3 study and encourage the continued development of pepinemab in patients diagnosed with EM HD.
Choosing Wisely for oncology in Brazil: 10 recommendations to deliver evidence-based cancer care
Authors: Fabio Ynoe de Moraes, Gustavo Nader Marta, Gunita Mitera, Daniel Neves Forte, Rodrigo Nascimento Pinheiro, Nivaldo F. Vieira, Rafael Gadia, Maira Caleffi, Patricia Chiappin Kauer, Luciana Holtz de Camargo Barros, Clarissa Mathias, Karina Gondim Moutinho da Conceicao Vasconcelos, Christopher Booth, Gustavo dos Santos Fernandes
Journal: Nature Medicine (2022)
Trastuzumab deruxtecan in HER2-positive breast cancer with brain metastases: a single-arm, phase 2 trial
Authors: Rupert Bartsch, Anna Sophie Berghoff, Julia Furtner, Maximilian Marhold, Elisabeth Sophie Bergen, Sophie Roider-Schur, Angelika Martina Starzer, Heidrun Forstner, Beate Rottenmanner, Karin Dieckmann, Zsuzsanna Bago-Horvath, Helmuth Haslacher, Georg Widhalm, Aysegül Ilhan-Mutlu, Christoph Minichsdorfer, Thorsten Fuereder, Thomas Szekeres, Leopold Oehler, Birgit Gruenberger, Christian F. Singer, Ansgar Weltermann, Rainer Puhr, Matthias Preusser
Journal: Nature Medicine (2022)

Abstract Trastuzumab deruxtecan is an antibody–drug conjugate with high extracranial activity in human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer. We conducted the prospective, open-label, single-arm, phase 2 TUXEDO-1 trial. We enrolled patients aged ≥18 years with HER2-positive breast cancer and newly diagnosed untreated brain metastases or brain metastases progressing after previous local therapy, previous exposure to trastuzumab and pertuzumab and no indication for immediate local therapy. Patients received trastuzumab deruxtecan intravenously at the standard dose of 5.4 mg per kg bodyweight once every 3 weeks. The primary endpoint was intracranial response rate measured according to the response assessment in neuro-oncology brain metastases criteria. A Simon two-stage design was used to compare a null hypothesis of &lt;26% response rate against an alternative of 61%. Fifteen patients were enrolled in the intention-to-treat population of patients who received at least one dose of study drug. Two patients (13.3%) had a complete intracranial response, nine (60%) had a partial intracranial response and three (20%) had stable disease as the best intracranial response, with a best overall intracranial response rate of 73.3% (95% confidential interval 48.1–89.1%), thus meeting the predefined primary outcome. No new safety signals were observed and global quality-of-life and cognitive functioning were maintained over the treatment duration. In the TUXEDO-1 trial (NCT04752059, EudraCT 2020-000981-41), trastuzumab deruxtecan showed a high intracranial response rate in patients with active brain metastases from HER2-positive breast cancer and should be considered as a treatment option in this setting.